Raj-Maharajwala
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README.md
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- **Model Type:** Instruction-tuned Language Model
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- **Base Model:** nvidia/Llama3-ChatQA-1.5-8B
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- **Finetuned Model:** Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B
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- **Model Architecture:** Llama
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- **Parameters:** 8.05 billion
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- **Developer:** Raj Maharajwala
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- **License:** llama3
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- **Language:** English
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### Quantized Model
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Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B-GGUF: https://huggingface.co/Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B-GGUF
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## Training Data
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The model has been fine-tuned on the InsuranceQA dataset, which contains insurance-specific question-answer pairs and domain knowledge.
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trainable params: 20.97M || all params: 8.05B || trainable%: 0.26%
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## Model Architecture
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The model uses the Llama 3 architecture with the following key components:
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- **Model Type:** Instruction-tuned Language Model
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- **Base Model:** nvidia/Llama3-ChatQA-1.5-8B
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- **Finetuned Model:** Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B
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- **Quantized Model:** Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B-GGUF
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- **Model Architecture:** Llama
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- **Parameters:** 8.05 billion
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- **Developer:** Raj Maharajwala
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- **License:** llama3
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- **Language:** English
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### Quantized Model
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Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B-GGUF: https://huggingface.co/Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B-GGUF
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## Training Data
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The model has been fine-tuned on the InsuranceQA dataset using LoRA (8 bit), which contains insurance-specific question-answer pairs and domain knowledge.
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trainable params: 20.97M || all params: 8.05B || trainable %: 0.26%
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```bash
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LoraConfig(
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r=8,
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lora_alpha=32,
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lora_dropout=0.05,
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bias="none",
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task_type="CAUSAL_LM",
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target_modules=['up_proj', 'down_proj', 'gate_proj', 'k_proj', 'q_proj', 'v_proj', 'o_proj']
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)
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/66315b34b1c6e12e1c304bf8/ZzHaMo1Kt9XNnFh24H3gt.png)
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/66315b34b1c6e12e1c304bf8/0sLiphsQL-j5km4c5_vru.png)
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```
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## Model Architecture
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The model uses the Llama 3 architecture with the following key components:
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